SCN5A (Nav1.5): Predicting the Consequence of Missense Single- Nucleotide Polymorphisms.

项目摘要/摘要 候选人背景：在弗吉尼亚大学研究生院，我在本科的基础上 利用光谱工具进行光谱学教育，以研究膜蛋白的柔韧性。作为一名 作为范德比尔特大学的博士后，我过渡到与人类有关的膜蛋白结构生物学 疾病，特别是KCNQ和KCNE家族相关的通道病。作为博士后研究员，我有 参与了几个有关疾病机制结构基础的项目，最近的一次是 提出了一种通过雌激素诱导的KCNQ1-1缺失来减少心尖氯分泌的机制 KCNE3通道传导。 研究策略：人类电压门控钠通道NaV1.5(由SCN5A编码)与 几种心脏疾病，包括扩张型心肌病，心脏传导疾病，病态窦房结综合征， 3型长QT间期综合征和Brugada综合征。几种算法准确地预测了SCN5A变体 最终是有害的(SIFT、PolyPhen-2、PredSNP等)。然而，在负面方面存在着显著的差距 这些方法的预测能力，即准确地将变异分类为良性的能力。我所采取的方式 建议从两个方面解决这一问题：1)纳入特定渠道、信息丰富的量化 将数据输入预测模型构建-目标是预测通道功能，而不是疾病- 诱发倾向-以及2)包括一组富含WT/中性表型的点突变变体 提高模型训练和评估过程中的辨别力。该项目旨在最终预测NaV1.5 平衡分析所有可能的氨基酸改变单核苷酸多态(NsSNP)的通道表型 模型系统的高通量计算和严格的实验验证：预测近 15,000个可能的SCN5A错义nsSNP目前仅在Silico中可行，即利用可计算 特定于通道的蛋白质序列和基于结构的特征。高吞吐量的可用性 电生理学仪器允许前所未有的离子通道功能输出从 异源表达NaV1.5；评估SCN5A变异对更自然的人动作电位的影响 就像人类诱导的多能干细胞一样，心肌细胞也有可能在低通量条件下培养。在接受指导的过程中 (K99)本奖项阶段，我将产生(误)贩卖和电生理电流输出数据 错义SCN5A的nsSNPs，重点是结构域IV(目标1)的电压传感模块(VSM)，并训练 SCN5A VSM IV特异性表型预测模型使用来自AIM 1和 文学(目标2)。作为一名独立的调查员，我将确定结构和灵活性引发的变化 使用Rosetta建模和核磁共振(核磁共振)的组合从选定的变体中提炼 预测模型(目标3)。 职业发展和培训：我的培训计划雄心勃勃，涵盖了几个学科，其中一些 这对我来说是新的。我将获得的技能是开发离子通道的计算预测模型 表型，通过荧光激活细胞分选(FACS)进行运输/表达定量， CRISPR/Cas9基因操作，以及HiPSC心肌细胞的产生。虽然有很多活动 按照计划，我将直接在各自领域的杰出科学家的实验室接受培训：查尔斯 桑德斯、延斯·梅勒和丹·罗登。

Project Summary/Abstract Candidate Background: In graduate school at the University of Virginia, I built on my undergraduate spectroscopy education by using spectroscopic tools to investigate membrane protein flexibility. As a Postdoctoral Fellow at Vanderbilt, I transitioned to membrane protein structural biology involved in human disease, specifically KCNQ and KCNE family-associated channelopathies. As a Postdoctoral Fellow, I have been involved in several projects concerning the structural underpinnings of disease mechanisms, most recently proposing a mechanism for diminished apical chloride secretion through an estrogen-induced loss of KCNQ1- KCNE3 channel conduction. Research Strategy: The human voltage-gated sodium channel Nav1.5 (encoded by SCN5A) is implicated in several diseases of the heart including dilated cardiomyopathy, cardiac conduction disease, sick sinus syndrome, type 3 longQT syndrome, and Brugada syndrome. Several algorithms accurately predict SCN5A variants that are ultimately harmful (SIFT, PolyPhen-2, PredSNP, etc.). However, there is a significant gap in the negative predictive ability of these methods, i.e. the ability to accurately classify a variant as benign. The approach I am proposing is to tackle this problem on two fronts: 1) incorporating channel-specific, quantitative information-rich data into predictive model construction—the objective being to predict channel function, instead of disease- inducing propensity—and 2) including a set of point mutation variants enriched in WT/neutral phenotypes to improve discrimination power during model training and evaluation. This project aims to ultimately predict Nav1.5 channel phenotypes for all possible amino-acid changing single nucleotide polymorphisms (nsSNP) by balancing high-throughput computation and rigorous experimental validation with model systems: predicting the nearly 15,000 possible SCN5A missense nsSNPs is currently only feasible in silico, i.e. leveraging calculable channel-specific protein sequence and structure-based features. The availability of a high-throughput electrophysiology instrument allows for an unprecedented amassing of ion channel functional output from heterologously expressed Nav1.5; the evaluation of SCN5A variants impact on action potential in the more native like human induced pluripotent stem cell cardiomyocytes is possible in low-throughput. During the mentored (K99) phase of this award, I will generate (mis)trafficking and electrophysiology current output data from missense nsSNPs of SCN5A, focusing on the Voltage-Sensing Module (VSM) of domain IV (Aim 1) and train an SCN5A VSM IV-specific phenotype prediction model using trafficking and electrophysiology data from Aim 1 and the literature (Aim 2). As an independent investigator, I will determine structure and flexibility-induced changes from selected variants using a combination of Rosetta modeling and nuclear magnetic resonance (NMR) to refine the predictive model (Aim 3). Career Development and Training: My training proposal is ambitious covering several disciplines, some of which will be new to me. The skills I will acquire are developing computational predictive models of ion channel phenotypes, trafficking/expression quantitation through Fluorescence Activated Cell Sorting (FACS), CRISPR/Cas9 gene manipulation, and hiPSC cardiomyocyte production. Though there are many activities planned, I will be trained directly in the laboratories of prominent scientists in their respective fields: Charles Sanders, Jens Meiler, and Dan Roden.